New generation of one-dimensional photonic crystal cavities as robust high-efficient frequency converter
Source abstract
Abstract
Abstract An effective method is proposed to design finite one-dimensional photonic crystal cavities (PhCCs) as robust high-efficient frequency converter. For this purpose, we consider two groups of PhCCs which are constructed by stacking m nonlinear (LiNbO3) and n linear (air) layers with variable thicknesses. In the first group, the number of linear layers is less than the nonlinear layers by one and in the second group by two. The conversion efficiency is calculated as a function of the arrangement and thicknesses of the linear and nonlinear layers by benefiting from nonlinear transfer matrix method. Our numerical simulations show that for each group of PhCCs, there is a structural formula by which the configurations with the highest efficiency can be constructed for any values of m and n (i.e. any number of layers). The efficient configurations are equivalent to Fabry–Perot cavities that depend on the relationship between m and n and the mirrors in two sides of these cavities can be periodic or nonperiodic. The conversion efficiencies of these designed PhCCs are more than 5 orders of magnitude higher than the perfect ones which satisfy photonic bandgap edge and quasi-phase matching. Moreover, the results reveal that conversion efficiencies of Fabry–Perot cavities with non-periodic mirrors are one order of magnitude higher than those with periodic mirrors. The major physical mechanisms of the enhancement are quasi-phase matching effect, cavity effect induced by dispersive mirrors, and double resonance for the pump and the harmonic fields in defect state. We believe that this method is very beneficial to the design of high-efficient compact optical frequency converters.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Photonic Crystals and Applications · Photonic and Optical Devices · Photorefractive and Nonlinear Optics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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